Overview

Dataset statistics

Number of variables42
Number of observations311029
Missing cells0
Missing cells (%)0.0%
Duplicate rows10855
Duplicate rows (%)3.5%
Total size in memory162.2 MiB
Average record size in memory546.7 B

Variable types

Numeric26
Categorical16

Alerts

num_outbound_cmds has constant value "0" Constant
Dataset has 10855 (3.5%) duplicate rowsDuplicates
service has a high cardinality: 65 distinct values High cardinality
src_bytes is highly correlated with srv_count and 9 other fieldsHigh correlation
dst_bytes is highly correlated with logged_in and 4 other fieldsHigh correlation
hot is highly correlated with num_compromised and 1 other fieldsHigh correlation
logged_in is highly correlated with dst_bytes and 3 other fieldsHigh correlation
num_compromised is highly correlated with hotHigh correlation
root_shell is highly correlated with num_rootHigh correlation
num_root is highly correlated with root_shellHigh correlation
is_guest_login is highly correlated with hotHigh correlation
count is highly correlated with dst_bytes and 3 other fieldsHigh correlation
srv_count is highly correlated with src_bytes and 6 other fieldsHigh correlation
serror_rate is highly correlated with srv_serror_rate and 3 other fieldsHigh correlation
srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
rerror_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
srv_rerror_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
same_srv_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_count is highly correlated with dst_bytes and 2 other fieldsHigh correlation
dst_host_srv_count is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with dst_bytes and 2 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 8 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
hot is highly correlated with num_file_creationsHigh correlation
logged_in is highly correlated with count and 1 other fieldsHigh correlation
num_compromised is highly correlated with su_attempted and 1 other fieldsHigh correlation
su_attempted is highly correlated with num_compromised and 1 other fieldsHigh correlation
num_root is highly correlated with num_compromised and 1 other fieldsHigh correlation
num_file_creations is highly correlated with hotHigh correlation
count is highly correlated with logged_in and 2 other fieldsHigh correlation
srv_count is highly correlated with count and 3 other fieldsHigh correlation
serror_rate is highly correlated with srv_serror_rate and 2 other fieldsHigh correlation
srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 5 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 5 other fieldsHigh correlation
same_srv_rate is highly correlated with rerror_rate and 6 other fieldsHigh correlation
diff_srv_rate is highly correlated with dst_host_diff_srv_rateHigh correlation
dst_host_count is highly correlated with logged_inHigh correlation
dst_host_srv_count is highly correlated with srv_count and 7 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with srv_count and 7 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with diff_srv_rateHigh correlation
dst_host_same_src_port_rate is highly correlated with count and 4 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 5 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 5 other fieldsHigh correlation
src_bytes is highly correlated with same_srv_rate and 5 other fieldsHigh correlation
dst_bytes is highly correlated with logged_in and 3 other fieldsHigh correlation
hot is highly correlated with num_compromised and 1 other fieldsHigh correlation
logged_in is highly correlated with dst_bytes and 2 other fieldsHigh correlation
num_compromised is highly correlated with hotHigh correlation
root_shell is highly correlated with num_rootHigh correlation
num_root is highly correlated with root_shellHigh correlation
is_guest_login is highly correlated with hotHigh correlation
count is highly correlated with dst_bytes and 2 other fieldsHigh correlation
srv_count is highly correlated with count and 3 other fieldsHigh correlation
serror_rate is highly correlated with srv_serror_rate and 2 other fieldsHigh correlation
srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 7 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 7 other fieldsHigh correlation
same_srv_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_count is highly correlated with dst_bytes and 2 other fieldsHigh correlation
dst_host_srv_count is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with src_bytes and 8 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with dst_bytes and 2 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 8 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 7 other fieldsHigh correlation
root_shell is highly correlated with num_outbound_cmdsHigh correlation
is_guest_login is highly correlated with service and 1 other fieldsHigh correlation
num_shells is highly correlated with num_outbound_cmdsHigh correlation
service is highly correlated with is_guest_login and 4 other fieldsHigh correlation
deu_ruim_ou_nao is highly correlated with service and 3 other fieldsHigh correlation
is_host_login is highly correlated with num_outbound_cmdsHigh correlation
num_outbound_cmds is highly correlated with root_shell and 14 other fieldsHigh correlation
num_failed_logins is highly correlated with num_outbound_cmdsHigh correlation
flag is highly correlated with num_outbound_cmdsHigh correlation
logged_in is highly correlated with service and 3 other fieldsHigh correlation
protocol_type is highly correlated with service and 3 other fieldsHigh correlation
land is highly correlated with num_outbound_cmdsHigh correlation
num_access_files is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
urgent is highly correlated with num_outbound_cmdsHigh correlation
wrong_fragment is highly correlated with num_outbound_cmdsHigh correlation
su_attempted is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
duration is highly correlated with num_file_creationsHigh correlation
protocol_type is highly correlated with service and 12 other fieldsHigh correlation
service is highly correlated with protocol_type and 22 other fieldsHigh correlation
flag is highly correlated with protocol_type and 13 other fieldsHigh correlation
wrong_fragment is highly correlated with dst_host_srv_diff_host_rateHigh correlation
urgent is highly correlated with root_shell and 1 other fieldsHigh correlation
hot is highly correlated with num_file_creationsHigh correlation
num_failed_logins is highly correlated with serviceHigh correlation
logged_in is highly correlated with service and 6 other fieldsHigh correlation
num_compromised is highly correlated with su_attempted and 2 other fieldsHigh correlation
root_shell is highly correlated with urgent and 1 other fieldsHigh correlation
su_attempted is highly correlated with num_compromised and 2 other fieldsHigh correlation
num_root is highly correlated with urgent and 3 other fieldsHigh correlation
num_file_creations is highly correlated with duration and 1 other fieldsHigh correlation
num_shells is highly correlated with root_shellHigh correlation
num_access_files is highly correlated with num_compromised and 2 other fieldsHigh correlation
is_guest_login is highly correlated with serviceHigh correlation
count is highly correlated with protocol_type and 14 other fieldsHigh correlation
srv_count is highly correlated with protocol_type and 8 other fieldsHigh correlation
serror_rate is highly correlated with flag and 6 other fieldsHigh correlation
srv_serror_rate is highly correlated with flag and 6 other fieldsHigh correlation
rerror_rate is highly correlated with protocol_type and 13 other fieldsHigh correlation
srv_rerror_rate is highly correlated with protocol_type and 12 other fieldsHigh correlation
same_srv_rate is highly correlated with protocol_type and 12 other fieldsHigh correlation
diff_srv_rate is highly correlated with service and 4 other fieldsHigh correlation
srv_diff_host_rate is highly correlated with service and 2 other fieldsHigh correlation
dst_host_count is highly correlated with service and 4 other fieldsHigh correlation
dst_host_srv_count is highly correlated with protocol_type and 13 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with protocol_type and 13 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with service and 3 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with protocol_type and 9 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with service and 1 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with service and 8 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with service and 9 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with protocol_type and 14 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with protocol_type and 11 other fieldsHigh correlation
deu_ruim_ou_nao is highly correlated with protocol_type and 10 other fieldsHigh correlation
duration is highly skewed (γ1 = 55.70336655) Skewed
src_bytes is highly skewed (γ1 = 432.7522283) Skewed
dst_bytes is highly skewed (γ1 = 185.3375224) Skewed
hot is highly skewed (γ1 = 136.2931811) Skewed
num_compromised is highly skewed (γ1 = 339.3717648) Skewed
num_root is highly skewed (γ1 = 338.8591352) Skewed
num_file_creations is highly skewed (γ1 = 460.8670439) Skewed
dst_host_srv_diff_host_rate is highly skewed (γ1 = 21.57807214) Skewed
duration has 298054 (95.8%) zeros Zeros
src_bytes has 62903 (20.2%) zeros Zeros
dst_bytes has 235821 (75.8%) zeros Zeros
hot has 309127 (99.4%) zeros Zeros
num_compromised has 309911 (99.6%) zeros Zeros
num_root has 310972 (> 99.9%) zeros Zeros
num_file_creations has 310944 (> 99.9%) zeros Zeros
serror_rate has 289894 (93.2%) zeros Zeros
srv_serror_rate has 291623 (93.8%) zeros Zeros
rerror_rate has 265596 (85.4%) zeros Zeros
srv_rerror_rate has 266116 (85.6%) zeros Zeros
diff_srv_rate has 248855 (80.0%) zeros Zeros
srv_diff_host_rate has 288817 (92.9%) zeros Zeros
dst_host_same_srv_rate has 5710 (1.8%) zeros Zeros
dst_host_diff_srv_rate has 209496 (67.4%) zeros Zeros
dst_host_same_src_port_rate has 107423 (34.5%) zeros Zeros
dst_host_srv_diff_host_rate has 281535 (90.5%) zeros Zeros
dst_host_serror_rate has 287112 (92.3%) zeros Zeros
dst_host_srv_serror_rate has 289809 (93.2%) zeros Zeros
dst_host_rerror_rate has 255123 (82.0%) zeros Zeros
dst_host_srv_rerror_rate has 262774 (84.5%) zeros Zeros

Reproduction

Analysis started2021-12-24 20:25:08.507291
Analysis finished2021-12-24 20:29:21.282988
Duration4 minutes and 12.78 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

duration
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct745
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.90273576
Minimum0
Maximum57715
Zeros298054
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:21.368289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum57715
Range57715
Interquartile range (IQR)0

Descriptive statistics

Standard deviation407.6444
Coefficient of variation (CV)22.76995011
Kurtosis5711.072975
Mean17.90273576
Median Absolute Deviation (MAD)0
Skewness55.70336655
Sum5568270
Variance166173.9568
MonotonicityNot monotonic
2021-12-24T21:29:21.511193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0298054
95.8%
16008
 
1.9%
43320
 
1.1%
280525
 
0.2%
5289
 
0.1%
3261
 
0.1%
282259
 
0.1%
2204
 
0.1%
281158
 
0.1%
28389
 
< 0.1%
Other values (735)1862
 
0.6%
ValueCountFrequency (%)
0298054
95.8%
16008
 
1.9%
2204
 
0.1%
3261
 
0.1%
43320
 
1.1%
5289
 
0.1%
668
 
< 0.1%
740
 
< 0.1%
829
 
< 0.1%
930
 
< 0.1%
ValueCountFrequency (%)
577151
< 0.1%
544511
< 0.1%
537711
< 0.1%
530111
< 0.1%
471141
< 0.1%
426891
< 0.1%
387011
< 0.1%
221741
< 0.1%
207411
< 0.1%
165001
< 0.1%

protocol_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
icmp
164969 
tcp
119357 
udp
26703 

Length

Max length4
Median length4
Mean length3.530397487
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowudp
2nd rowudp
3rd rowudp
4th rowudp
5th rowudp

Common Values

ValueCountFrequency (%)
icmp164969
53.0%
tcp119357
38.4%
udp26703
 
8.6%

Length

2021-12-24T21:29:21.682307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:21.812915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
icmp164969
53.0%
tcp119357
38.4%
udp26703
 
8.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

service
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 MiB
ecr_i
164352 
private
78510 
http
41237 
smtp
 
8268
pop_3
 
3972
Other values (60)
 
14690

Length

Max length11
Median length5
Mean length5.406450845
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowprivate
2nd rowprivate
3rd rowprivate
4th rowprivate
5th rowprivate

Common Values

ValueCountFrequency (%)
ecr_i164352
52.8%
private78510
25.2%
http41237
 
13.3%
smtp8268
 
2.7%
pop_33972
 
1.3%
domain_u3160
 
1.0%
ftp_data2223
 
0.7%
other2185
 
0.7%
telnet2077
 
0.7%
ftp837
 
0.3%
Other values (55)4208
 
1.4%

Length

2021-12-24T21:29:21.909407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ecr_i164352
52.8%
private78510
25.2%
http41237
 
13.3%
smtp8268
 
2.7%
pop_33972
 
1.3%
domain_u3160
 
1.0%
ftp_data2223
 
0.7%
other2185
 
0.7%
telnet2077
 
0.7%
ftp837
 
0.3%
Other values (55)4208
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

flag
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.5 MiB
SF
248379 
REJ
41945 
S0
 
18012
RSTO
 
1393
RSTR
 
872
Other values (6)
 
428

Length

Max length6
Median length2
Mean length2.149461947
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF248379
79.9%
REJ41945
 
13.5%
S018012
 
5.8%
RSTO1393
 
0.4%
RSTR872
 
0.3%
S3289
 
0.1%
SH84
 
< 0.1%
S127
 
< 0.1%
S222
 
< 0.1%
OTH4
 
< 0.1%

Length

2021-12-24T21:29:22.009198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf248379
79.9%
rej41945
 
13.5%
s018012
 
5.8%
rsto1393
 
0.4%
rstr872
 
0.3%
s3289
 
0.1%
sh84
 
< 0.1%
s127
 
< 0.1%
s222
 
< 0.1%
oth4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

src_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2504
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731.702192
Minimum0
Maximum62825648
Zeros62903
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:22.125546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1105
median520
Q31032
95-th percentile1032
Maximum62825648
Range62825648
Interquartile range (IQR)927

Descriptive statistics

Standard deviation127656.7311
Coefficient of variation (CV)73.71748541
Kurtosis200756.1771
Mean1731.702192
Median Absolute Deviation (MAD)512
Skewness432.7522283
Sum538609601
Variance1.629624098 × 1010
MonotonicityNot monotonic
2021-12-24T21:29:22.256875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1032108627
34.9%
062903
20.2%
52048588
15.6%
10519616
 
6.3%
5086265
 
2.0%
25995000
 
1.6%
302371
 
0.8%
441169
 
0.4%
545401061
 
0.3%
45976
 
0.3%
Other values (2494)54453
17.5%
ValueCountFrequency (%)
062903
20.2%
1602
 
0.2%
21
 
< 0.1%
514
 
< 0.1%
626
 
< 0.1%
744
 
< 0.1%
894
 
< 0.1%
957
 
< 0.1%
1070
 
< 0.1%
1110
 
< 0.1%
ValueCountFrequency (%)
628256481
 
< 0.1%
316456081
 
< 0.1%
62916681
 
< 0.1%
38869541
 
< 0.1%
31314641
 
< 0.1%
21946191
 
< 0.1%
12627961
 
< 0.1%
5017604
< 0.1%
2948121
 
< 0.1%
2860401
 
< 0.1%

dst_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct9202
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.9936855
Minimum0
Maximum5203179
Zeros235821
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:22.394204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2698
Maximum5203179
Range5203179
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16120.17952
Coefficient of variation (CV)21.55122407
Kurtosis46092.23264
Mean747.9936855
Median Absolute Deviation (MAD)0
Skewness185.3375224
Sum232647728
Variance259860187.7
MonotonicityNot monotonic
2021-12-24T21:29:22.510759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0235821
75.8%
1468558
 
2.8%
1475557
 
1.8%
2934988
 
1.6%
933600
 
1.2%
1051240
 
0.4%
441065
 
0.3%
83141053
 
0.3%
42675
 
0.2%
174591
 
0.2%
Other values (9192)47881
 
15.4%
ValueCountFrequency (%)
0235821
75.8%
1478
 
0.2%
36
 
< 0.1%
436
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
1212
 
< 0.1%
15221
 
0.1%
161
 
< 0.1%
1715
 
< 0.1%
ValueCountFrequency (%)
52031791
< 0.1%
30349291
< 0.1%
28811121
< 0.1%
20992471
< 0.1%
18885251
< 0.1%
18680801
< 0.1%
18333941
< 0.1%
16284891
< 0.1%
16126661
< 0.1%
13459271
< 0.1%

land
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311020 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311020
> 99.9%
19
 
< 0.1%

Length

2021-12-24T21:29:22.639441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:22.742628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311020
> 99.9%
19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

wrong_fragment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
310882 
1
 
102
3
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0310882
> 99.9%
1102
 
< 0.1%
345
 
< 0.1%

Length

2021-12-24T21:29:22.831809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:22.895979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0310882
> 99.9%
1102
 
< 0.1%
345
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

urgent
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311019 
1
 
5
2
 
4
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311019
> 99.9%
15
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%

Length

2021-12-24T21:29:22.958500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:23.028544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311019
> 99.9%
15
 
< 0.1%
24
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01467708799
Minimum0
Maximum101
Zeros309127
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:23.102910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum101
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3120684661
Coefficient of variation (CV)21.26228761
Kurtosis37056.94357
Mean0.01467708799
Median Absolute Deviation (MAD)0
Skewness136.2931811
Sum4565
Variance0.09738672754
MonotonicityNot monotonic
2021-12-24T21:29:23.191120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0309127
99.4%
21633
 
0.5%
1124
 
< 0.1%
454
 
< 0.1%
524
 
< 0.1%
614
 
< 0.1%
312
 
< 0.1%
712
 
< 0.1%
187
 
< 0.1%
196
 
< 0.1%
Other values (8)16
 
< 0.1%
ValueCountFrequency (%)
0309127
99.4%
1124
 
< 0.1%
21633
 
0.5%
312
 
< 0.1%
454
 
< 0.1%
524
 
< 0.1%
614
 
< 0.1%
712
 
< 0.1%
102
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
1011
 
< 0.1%
302
 
< 0.1%
243
< 0.1%
224
< 0.1%
196
< 0.1%
187
< 0.1%
151
 
< 0.1%
142
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%

num_failed_logins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
310307 
1
 
716
3
 
3
4
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0310307
99.8%
1716
 
0.2%
33
 
< 0.1%
42
 
< 0.1%
21
 
< 0.1%

Length

2021-12-24T21:29:23.291121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:23.345516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0310307
99.8%
1716
 
0.2%
33
 
< 0.1%
42
 
< 0.1%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

logged_in
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
257384 
1
53645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0257384
82.8%
153645
 
17.2%

Length

2021-12-24T21:29:23.429950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:23.492469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0257384
82.8%
153645
 
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_compromised
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01124332458
Minimum0
Maximum796
Zeros309911
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:23.561359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum796
Range796
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.958324881
Coefficient of variation (CV)174.1766741
Kurtosis122995.6495
Mean0.01124332458
Median Absolute Deviation (MAD)0
Skewness339.3717648
Sum3497
Variance3.835036338
MonotonicityNot monotonic
2021-12-24T21:29:23.661444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0309911
99.6%
11063
 
0.3%
213
 
< 0.1%
37
 
< 0.1%
45
 
< 0.1%
84
 
< 0.1%
73
 
< 0.1%
63
 
< 0.1%
53
 
< 0.1%
492
 
< 0.1%
Other values (14)15
 
< 0.1%
ValueCountFrequency (%)
0309911
99.6%
11063
 
0.3%
213
 
< 0.1%
37
 
< 0.1%
45
 
< 0.1%
53
 
< 0.1%
63
 
< 0.1%
73
 
< 0.1%
84
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
7961
< 0.1%
6111
< 0.1%
3811
< 0.1%
1651
< 0.1%
571
< 0.1%
492
< 0.1%
361
< 0.1%
251
< 0.1%
231
< 0.1%
161
< 0.1%

root_shell
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
310967 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0310967
> 99.9%
162
 
< 0.1%

Length

2021-12-24T21:29:23.939079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:23.993953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0310967
> 99.9%
162
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

su_attempted
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311024 
1
 
3
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311024
> 99.9%
13
 
< 0.1%
22
 
< 0.1%

Length

2021-12-24T21:29:24.060844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:24.110828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311024
> 99.9%
13
 
< 0.1%
22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_root
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008359349128
Minimum0
Maximum878
Zeros310972
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:24.179688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum878
Range878
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.165196054
Coefficient of variation (CV)259.0149091
Kurtosis122929.3633
Mean0.008359349128
Median Absolute Deviation (MAD)0
Skewness338.8591352
Sum2600
Variance4.688073954
MonotonicityNot monotonic
2021-12-24T21:29:24.280807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0310972
> 99.9%
121
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
25
 
< 0.1%
82
 
< 0.1%
72
 
< 0.1%
231
 
< 0.1%
6841
 
< 0.1%
4011
 
< 0.1%
Other values (11)11
 
< 0.1%
ValueCountFrequency (%)
0310972
> 99.9%
121
 
< 0.1%
25
 
< 0.1%
37
 
< 0.1%
46
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
8781
< 0.1%
6841
< 0.1%
4011
< 0.1%
1731
< 0.1%
1451
< 0.1%
511
< 0.1%
451
< 0.1%
311
< 0.1%
261
< 0.1%
231
< 0.1%

num_file_creations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009581100155
Minimum0
Maximum100
Zeros310944
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:24.374827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1931185118
Coefficient of variation (CV)201.5619382
Kurtosis233151.1115
Mean0.0009581100155
Median Absolute Deviation (MAD)0
Skewness460.8670439
Sum298
Variance0.03729475958
MonotonicityNot monotonic
2021-12-24T21:29:24.459784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0310944
> 99.9%
151
 
< 0.1%
215
 
< 0.1%
46
 
< 0.1%
35
 
< 0.1%
52
 
< 0.1%
301
 
< 0.1%
121
 
< 0.1%
61
 
< 0.1%
1001
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
0310944
> 99.9%
151
 
< 0.1%
215
 
< 0.1%
35
 
< 0.1%
46
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
301
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
52
 
< 0.1%
46
 
< 0.1%
35
 
< 0.1%
215
< 0.1%

num_shells
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311010 
1
 
15
2
 
3
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311010
> 99.9%
115
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%

Length

2021-12-24T21:29:24.545319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:24.614148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311010
> 99.9%
115
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_access_files
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
310800 
1
 
221
2
 
6
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0310800
99.9%
1221
 
0.1%
26
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

Length

2021-12-24T21:29:24.676631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:24.746605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0310800
99.9%
1221
 
0.1%
26
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_outbound_cmds
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311029 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311029
100.0%

Length

2021-12-24T21:29:24.831081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:24.877947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311029
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

is_host_login
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
311017 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0311017
> 99.9%
112
 
< 0.1%

Length

2021-12-24T21:29:24.948356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:25.010815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0311017
> 99.9%
112
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

is_guest_login
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
0
310275 
1
 
754

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0310275
99.8%
1754
 
0.2%

Length

2021-12-24T21:29:25.062780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:25.132960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0310275
99.8%
1754
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct502
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.2470188
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:25.196517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median212
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)494

Descriptive statistics

Standard deviation219.8344117
Coefficient of variation (CV)0.8164785362
Kurtosis-1.76974478
Mean269.2470188
Median Absolute Deviation (MAD)211
Skewness0.03577259576
Sum83743631
Variance48327.16859
MonotonicityNot monotonic
2021-12-24T21:29:25.326508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511102396
32.9%
129935
 
9.6%
51019852
 
6.4%
214481
 
4.7%
38309
 
2.7%
5097814
 
2.5%
45132
 
1.7%
52854
 
0.9%
62204
 
0.7%
72097
 
0.7%
Other values (492)115955
37.3%
ValueCountFrequency (%)
01
 
< 0.1%
129935
9.6%
214481
4.7%
38309
 
2.7%
45132
 
1.7%
52854
 
0.9%
62204
 
0.7%
72097
 
0.7%
81886
 
0.6%
91741
 
0.6%
ValueCountFrequency (%)
511102396
32.9%
51019852
 
6.4%
5097814
 
2.5%
5081839
 
0.6%
507380
 
0.1%
506118
 
< 0.1%
50527
 
< 0.1%
50461
 
< 0.1%
50330
 
< 0.1%
5024
 
< 0.1%

srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct471
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.5800392
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:25.467735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median126
Q3511
95-th percentile511
Maximum511
Range511
Interquartile range (IQR)504

Descriptive statistics

Standard deviation239.3080275
Coefficient of variation (CV)1.015824721
Kurtosis-1.874901796
Mean235.5800392
Median Absolute Deviation (MAD)125
Skewness0.2345783049
Sum73272224
Variance57268.33204
MonotonicityNot monotonic
2021-12-24T21:29:25.583346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511101997
32.8%
130852
 
9.9%
51019750
 
6.3%
217243
 
5.5%
311162
 
3.6%
5097632
 
2.5%
47502
 
2.4%
55488
 
1.8%
65319
 
1.7%
75201
 
1.7%
Other values (461)98883
31.8%
ValueCountFrequency (%)
01
 
< 0.1%
130852
9.9%
217243
5.5%
311162
 
3.6%
47502
 
2.4%
55488
 
1.8%
65319
 
1.7%
75201
 
1.7%
85077
 
1.6%
94539
 
1.5%
ValueCountFrequency (%)
511101997
32.8%
51019750
 
6.3%
5097632
 
2.5%
5081752
 
0.6%
507278
 
0.1%
506115
 
< 0.1%
50516
 
< 0.1%
50458
 
< 0.1%
50320
 
< 0.1%
5022
 
< 0.1%

serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05921528218
Minimum0
Maximum1
Zeros289894
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:25.719744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2338732464
Coefficient of variation (CV)3.949542041
Kurtosis12.08968864
Mean0.05921528218
Median Absolute Deviation (MAD)0
Skewness3.747182579
Sum18417.67
Variance0.05469669539
MonotonicityNot monotonic
2021-12-24T21:29:25.864679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0289894
93.2%
117741
 
5.7%
0.05368
 
0.1%
0.04311
 
0.1%
0.01231
 
0.1%
0.03230
 
0.1%
0.06221
 
0.1%
0.02216
 
0.1%
0.07160
 
0.1%
0.08131
 
< 0.1%
Other values (84)1526
 
0.5%
ValueCountFrequency (%)
0289894
93.2%
0.01231
 
0.1%
0.02216
 
0.1%
0.03230
 
0.1%
0.04311
 
0.1%
0.05368
 
0.1%
0.06221
 
0.1%
0.07160
 
0.1%
0.08131
 
< 0.1%
0.09131
 
< 0.1%
ValueCountFrequency (%)
117741
5.7%
0.922
 
< 0.1%
0.916
 
< 0.1%
0.99
 
< 0.1%
0.8912
 
< 0.1%
0.8814
 
< 0.1%
0.8714
 
< 0.1%
0.8620
 
< 0.1%
0.8529
 
< 0.1%
0.8417
 
< 0.1%

srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct93
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05919322636
Minimum0
Maximum1
Zeros291623
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:26.009520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2348175974
Coefficient of variation (CV)3.966967368
Kurtosis12.01655228
Mean0.05919322636
Median Absolute Deviation (MAD)0
Skewness3.739833612
Sum18410.81
Variance0.05513930404
MonotonicityNot monotonic
2021-12-24T21:29:26.127499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0291623
93.8%
117984
 
5.8%
0.03116
 
< 0.1%
0.0280
 
< 0.1%
0.0670
 
< 0.1%
0.0469
 
< 0.1%
0.0557
 
< 0.1%
0.3356
 
< 0.1%
0.549
 
< 0.1%
0.2547
 
< 0.1%
Other values (83)878
 
0.3%
ValueCountFrequency (%)
0291623
93.8%
0.0127
 
< 0.1%
0.0280
 
< 0.1%
0.03116
 
< 0.1%
0.0469
 
< 0.1%
0.0557
 
< 0.1%
0.0670
 
< 0.1%
0.0743
 
< 0.1%
0.0845
 
< 0.1%
0.0928
 
< 0.1%
ValueCountFrequency (%)
117984
5.8%
0.957
 
< 0.1%
0.9413
 
< 0.1%
0.935
 
< 0.1%
0.9213
 
< 0.1%
0.916
 
< 0.1%
0.99
 
< 0.1%
0.899
 
< 0.1%
0.8812
 
< 0.1%
0.878
 
< 0.1%

rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1425852573
Minimum0
Maximum1
Zeros265596
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:26.416620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3475638316
Coefficient of variation (CV)2.437586032
Kurtosis2.18529726
Mean0.1425852573
Median Absolute Deviation (MAD)0
Skewness2.041785469
Sum44348.15
Variance0.1208006171
MonotonicityNot monotonic
2021-12-24T21:29:26.579842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0265596
85.4%
141490
 
13.3%
0.95362
 
0.1%
0.96274
 
0.1%
0.25240
 
0.1%
0.33226
 
0.1%
0.98221
 
0.1%
0.97221
 
0.1%
0.99205
 
0.1%
0.94181
 
0.1%
Other values (84)2013
 
0.6%
ValueCountFrequency (%)
0265596
85.4%
0.082
 
< 0.1%
0.096
 
< 0.1%
0.111
 
< 0.1%
0.1116
 
< 0.1%
0.1216
 
< 0.1%
0.1313
 
< 0.1%
0.1420
 
< 0.1%
0.1528
 
< 0.1%
0.1617
 
< 0.1%
ValueCountFrequency (%)
141490
13.3%
0.99205
 
0.1%
0.98221
 
0.1%
0.97221
 
0.1%
0.96274
 
0.1%
0.95362
 
0.1%
0.94181
 
0.1%
0.93164
 
0.1%
0.92132
 
< 0.1%
0.91134
 
< 0.1%

srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1422476361
Minimum0
Maximum1
Zeros266116
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:26.717405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3482328548
Coefficient of variation (CV)2.448074811
Kurtosis2.205781393
Mean0.1422476361
Median Absolute Deviation (MAD)0
Skewness2.048369702
Sum44243.14
Variance0.1212661211
MonotonicityNot monotonic
2021-12-24T21:29:26.874470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0266116
85.6%
143449
 
14.0%
0.5139
 
< 0.1%
0.3386
 
< 0.1%
0.9777
 
< 0.1%
0.6775
 
< 0.1%
0.2553
 
< 0.1%
0.9850
 
< 0.1%
0.241
 
< 0.1%
0.7539
 
< 0.1%
Other values (87)904
 
0.3%
ValueCountFrequency (%)
0266116
85.6%
0.025
 
< 0.1%
0.038
 
< 0.1%
0.049
 
< 0.1%
0.0512
 
< 0.1%
0.0616
 
< 0.1%
0.0715
 
< 0.1%
0.0820
 
< 0.1%
0.099
 
< 0.1%
0.117
 
< 0.1%
ValueCountFrequency (%)
143449
14.0%
0.9850
 
< 0.1%
0.9777
 
< 0.1%
0.9621
 
< 0.1%
0.9522
 
< 0.1%
0.9437
 
< 0.1%
0.9318
 
< 0.1%
0.9217
 
< 0.1%
0.9113
 
< 0.1%
0.920
 
< 0.1%

same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8156535243
Minimum0
Maximum1
Zeros2902
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:27.011979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3716048413
Coefficient of variation (CV)0.4555915352
Kurtosis0.3870813182
Mean0.8156535243
Median Absolute Deviation (MAD)0
Skewness-1.536177436
Sum253691.9
Variance0.1380901581
MonotonicityNot monotonic
2021-12-24T21:29:27.128996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248870
80.0%
0.066037
 
1.9%
0.055723
 
1.8%
0.075435
 
1.7%
0.035410
 
1.7%
0.045282
 
1.7%
0.015123
 
1.6%
0.025099
 
1.6%
0.083383
 
1.1%
02902
 
0.9%
Other values (69)17765
 
5.7%
ValueCountFrequency (%)
02902
0.9%
0.015123
1.6%
0.025099
1.6%
0.035410
1.7%
0.045282
1.7%
0.055723
1.8%
0.066037
1.9%
0.075435
1.7%
0.083383
1.1%
0.092654
0.9%
ValueCountFrequency (%)
1248870
80.0%
0.9996
 
< 0.1%
0.9814
 
< 0.1%
0.975
 
< 0.1%
0.963
 
< 0.1%
0.953
 
< 0.1%
0.943
 
< 0.1%
0.934
 
< 0.1%
0.928
 
< 0.1%
0.912
 
< 0.1%

diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02444656286
Minimum0
Maximum1
Zeros248855
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:27.270728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1070606722
Coefficient of variation (CV)4.379375245
Kurtosis68.5477176
Mean0.02444656286
Median Absolute Deviation (MAD)0
Skewness8.080886973
Sum7603.59
Variance0.01146198754
MonotonicityNot monotonic
2021-12-24T21:29:27.401793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0248855
80.0%
0.0629995
 
9.6%
0.0717898
 
5.8%
0.083211
 
1.0%
0.053196
 
1.0%
12906
 
0.9%
0.091829
 
0.6%
0.1681
 
0.2%
0.67357
 
0.1%
0.5264
 
0.1%
Other values (90)1837
 
0.6%
ValueCountFrequency (%)
0248855
80.0%
0.0159
 
< 0.1%
0.0244
 
< 0.1%
0.0316
 
< 0.1%
0.046
 
< 0.1%
0.053196
 
1.0%
0.0629995
 
9.6%
0.0717898
 
5.8%
0.083211
 
1.0%
0.091829
 
0.6%
ValueCountFrequency (%)
12906
0.9%
0.995
 
< 0.1%
0.9835
 
< 0.1%
0.9762
 
< 0.1%
0.9633
 
< 0.1%
0.953
 
< 0.1%
0.946
 
< 0.1%
0.934
 
< 0.1%
0.923
 
< 0.1%
0.915
 
< 0.1%

srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02534905105
Minimum0
Maximum1
Zeros288817
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:27.533179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.13
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1252313346
Coefficient of variation (CV)4.940277028
Kurtosis42.12247471
Mean0.02534905105
Median Absolute Deviation (MAD)0
Skewness6.294879543
Sum7884.29
Variance0.01568288717
MonotonicityNot monotonic
2021-12-24T21:29:27.663215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0288817
92.9%
13330
 
1.1%
0.121093
 
0.4%
0.671073
 
0.3%
0.5992
 
0.3%
0.33880
 
0.3%
0.1844
 
0.3%
0.11839
 
0.3%
0.25798
 
0.3%
0.17733
 
0.2%
Other values (77)11630
 
3.7%
ValueCountFrequency (%)
0288817
92.9%
0.01306
 
0.1%
0.02151
 
< 0.1%
0.0384
 
< 0.1%
0.04152
 
< 0.1%
0.05336
 
0.1%
0.06470
 
0.2%
0.07622
 
0.2%
0.08675
 
0.2%
0.09693
 
0.2%
ValueCountFrequency (%)
13330
1.1%
0.964
 
< 0.1%
0.957
 
< 0.1%
0.946
 
< 0.1%
0.933
 
< 0.1%
0.929
 
< 0.1%
0.9110
 
< 0.1%
0.94
 
< 0.1%
0.8912
 
< 0.1%
0.889
 
< 0.1%

dst_host_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.282681
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:27.800880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q1255
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.9132982
Coefficient of variation (CV)0.258894101
Kurtosis7.309341114
Mean235.282681
Median Absolute Deviation (MAD)0
Skewness-2.978740705
Sum73179737
Variance3710.429897
MonotonicityNot monotonic
2021-12-24T21:29:27.917366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255277190
89.1%
11293
 
0.4%
2865
 
0.3%
3737
 
0.2%
4716
 
0.2%
5597
 
0.2%
6548
 
0.2%
8520
 
0.2%
7483
 
0.2%
9458
 
0.1%
Other values (246)27622
 
8.9%
ValueCountFrequency (%)
02
 
< 0.1%
11293
0.4%
2865
0.3%
3737
0.2%
4716
0.2%
5597
0.2%
6548
0.2%
7483
 
0.2%
8520
0.2%
9458
 
0.1%
ValueCountFrequency (%)
255277190
89.1%
25436
 
< 0.1%
25339
 
< 0.1%
25247
 
< 0.1%
25134
 
< 0.1%
25047
 
< 0.1%
24944
 
< 0.1%
24844
 
< 0.1%
24741
 
< 0.1%
24633
 
< 0.1%

dst_host_srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.1939144
Minimum0
Maximum255
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:28.050190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1244
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)11

Descriptive statistics

Standard deviation100.3064705
Coefficient of variation (CV)0.5035619225
Kurtosis-0.2334436373
Mean199.1939144
Median Absolute Deviation (MAD)0
Skewness-1.309420777
Sum61955084
Variance10061.38802
MonotonicityNot monotonic
2021-12-24T21:29:28.181170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255202427
65.1%
25413552
 
4.4%
2536746
 
2.2%
16068
 
2.0%
33373
 
1.1%
83360
 
1.1%
23337
 
1.1%
63261
 
1.0%
73260
 
1.0%
183252
 
1.0%
Other values (246)62393
 
20.1%
ValueCountFrequency (%)
02
 
< 0.1%
16068
2.0%
23337
1.1%
33373
1.1%
43175
1.0%
52899
0.9%
63261
1.0%
73260
1.0%
83360
1.1%
92886
0.9%
ValueCountFrequency (%)
255202427
65.1%
25413552
 
4.4%
2536746
 
2.2%
2522021
 
0.6%
2511123
 
0.4%
250479
 
0.2%
249588
 
0.2%
248449
 
0.1%
2471747
 
0.6%
2462908
 
0.9%

dst_host_same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7934942079
Minimum0
Maximum1
Zeros5710
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:28.297980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.97
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.387089986
Coefficient of variation (CV)0.4878296302
Kurtosis0.008421274347
Mean0.7934942079
Median Absolute Deviation (MAD)0
Skewness-1.401265041
Sum246799.71
Variance0.1498386573
MonotonicityNot monotonic
2021-12-24T21:29:28.429336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1222831
71.6%
0.028973
 
2.9%
0.078754
 
2.8%
0.048515
 
2.7%
0.998470
 
2.7%
0.058346
 
2.7%
0.016472
 
2.1%
0.036442
 
2.1%
05710
 
1.8%
0.065495
 
1.8%
Other values (91)21021
 
6.8%
ValueCountFrequency (%)
05710
1.8%
0.016472
2.1%
0.028973
2.9%
0.036442
2.1%
0.048515
2.7%
0.058346
2.7%
0.065495
1.8%
0.078754
2.8%
0.083209
 
1.0%
0.09114
 
< 0.1%
ValueCountFrequency (%)
1222831
71.6%
0.998470
 
2.7%
0.981516
 
0.5%
0.971911
 
0.6%
0.964180
 
1.3%
0.95363
 
0.1%
0.94180
 
0.1%
0.93254
 
0.1%
0.92150
 
< 0.1%
0.91191
 
0.1%

dst_host_diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024952625
Minimum0
Maximum1
Zeros209496
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:28.568016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.09600330516
Coefficient of variation (CV)3.847423074
Kurtosis80.62099477
Mean0.024952625
Median Absolute Deviation (MAD)0
Skewness8.631956643
Sum7760.99
Variance0.009216634602
MonotonicityNot monotonic
2021-12-24T21:29:28.684536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0209496
67.4%
0.0627288
 
8.8%
0.0125421
 
8.2%
0.0715396
 
5.0%
0.059623
 
3.1%
0.045847
 
1.9%
0.023545
 
1.1%
0.083333
 
1.1%
0.092640
 
0.8%
0.032064
 
0.7%
Other values (91)6376
 
2.0%
ValueCountFrequency (%)
0209496
67.4%
0.0125421
 
8.2%
0.023545
 
1.1%
0.032064
 
0.7%
0.045847
 
1.9%
0.059623
 
3.1%
0.0627288
 
8.8%
0.0715396
 
5.0%
0.083333
 
1.1%
0.092640
 
0.8%
ValueCountFrequency (%)
11811
0.6%
0.9967
 
< 0.1%
0.9855
 
< 0.1%
0.9720
 
< 0.1%
0.9641
 
< 0.1%
0.9554
 
< 0.1%
0.9478
 
< 0.1%
0.9372
 
< 0.1%
0.9255
 
< 0.1%
0.9133
 
< 0.1%

dst_host_same_src_port_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5479185864
Minimum0
Maximum1
Zeros107423
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:28.975388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.491962772
Coefficient of variation (CV)0.8978756776
Kurtosis-1.955744256
Mean0.5479185864
Median Absolute Deviation (MAD)0
Skewness-0.1802871667
Sum170418.57
Variance0.242027369
MonotonicityNot monotonic
2021-12-24T21:29:29.101132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1167127
53.7%
0107423
34.5%
0.0116624
 
5.3%
0.024313
 
1.4%
0.032610
 
0.8%
0.041618
 
0.5%
0.051247
 
0.4%
0.061007
 
0.3%
0.08724
 
0.2%
0.07714
 
0.2%
Other values (91)7622
 
2.5%
ValueCountFrequency (%)
0107423
34.5%
0.0116624
 
5.3%
0.024313
 
1.4%
0.032610
 
0.8%
0.041618
 
0.5%
0.051247
 
0.4%
0.061007
 
0.3%
0.07714
 
0.2%
0.08724
 
0.2%
0.09457
 
0.1%
ValueCountFrequency (%)
1167127
53.7%
0.99150
 
< 0.1%
0.9836
 
< 0.1%
0.9751
 
< 0.1%
0.9637
 
< 0.1%
0.9551
 
< 0.1%
0.9456
 
< 0.1%
0.9375
 
< 0.1%
0.9228
 
< 0.1%
0.9130
 
< 0.1%

dst_host_srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004566230159
Minimum0
Maximum1
Zeros281535
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:29.248673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03577328444
Coefficient of variation (CV)7.834314784
Kurtosis552.8429196
Mean0.004566230159
Median Absolute Deviation (MAD)0
Skewness21.57807214
Sum1420.23
Variance0.00127972788
MonotonicityNot monotonic
2021-12-24T21:29:29.386360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0281535
90.5%
0.027504
 
2.4%
0.015961
 
1.9%
0.044624
 
1.5%
0.034484
 
1.4%
0.052783
 
0.9%
0.061327
 
0.4%
0.07806
 
0.3%
0.08293
 
0.1%
1266
 
0.1%
Other values (48)1446
 
0.5%
ValueCountFrequency (%)
0281535
90.5%
0.015961
 
1.9%
0.027504
 
2.4%
0.034484
 
1.4%
0.044624
 
1.5%
0.052783
 
0.9%
0.061327
 
0.4%
0.07806
 
0.3%
0.08293
 
0.1%
0.09175
 
0.1%
ValueCountFrequency (%)
1266
0.1%
0.81
 
< 0.1%
0.753
 
< 0.1%
0.6737
 
< 0.1%
0.621
 
< 0.1%
0.66
 
< 0.1%
0.574
 
< 0.1%
0.564
 
< 0.1%
0.554
 
< 0.1%
0.543
 
< 0.1%

dst_host_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05876419884
Minimum0
Maximum1
Zeros287112
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:29.531317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2312955193
Coefficient of variation (CV)3.935993749
Kurtosis12.32319989
Mean0.05876419884
Median Absolute Deviation (MAD)0
Skewness3.771039409
Sum18277.37
Variance0.05349761724
MonotonicityNot monotonic
2021-12-24T21:29:29.652272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0287112
92.3%
116921
 
5.4%
0.012027
 
0.7%
0.02718
 
0.2%
0.05418
 
0.1%
0.04334
 
0.1%
0.03251
 
0.1%
0.07210
 
0.1%
0.06173
 
0.1%
0.33165
 
0.1%
Other values (90)2700
 
0.9%
ValueCountFrequency (%)
0287112
92.3%
0.012027
 
0.7%
0.02718
 
0.2%
0.03251
 
0.1%
0.04334
 
0.1%
0.05418
 
0.1%
0.06173
 
0.1%
0.07210
 
0.1%
0.0872
 
< 0.1%
0.0976
 
< 0.1%
ValueCountFrequency (%)
116921
5.4%
0.99133
 
< 0.1%
0.9837
 
< 0.1%
0.9741
 
< 0.1%
0.9641
 
< 0.1%
0.953
 
< 0.1%
0.942
 
< 0.1%
0.921
 
< 0.1%
0.918
 
< 0.1%
0.93
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05879117381
Minimum0
Maximum1
Zeros289809
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:29.799864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2329971147
Coefficient of variation (CV)3.963130851
Kurtosis12.15736563
Mean0.05879117381
Median Absolute Deviation (MAD)0
Skewness3.753758323
Sum18285.76
Variance0.05428765547
MonotonicityNot monotonic
2021-12-24T21:29:29.935309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0289809
93.2%
117444
 
5.6%
0.011720
 
0.6%
0.02306
 
0.1%
0.0563
 
< 0.1%
0.0360
 
< 0.1%
0.551
 
< 0.1%
0.0648
 
< 0.1%
0.7538
 
< 0.1%
0.835
 
< 0.1%
Other values (91)1455
 
0.5%
ValueCountFrequency (%)
0289809
93.2%
0.011720
 
0.6%
0.02306
 
0.1%
0.0360
 
< 0.1%
0.0430
 
< 0.1%
0.0563
 
< 0.1%
0.0648
 
< 0.1%
0.0727
 
< 0.1%
0.0822
 
< 0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
117444
5.6%
0.992
 
< 0.1%
0.9817
 
< 0.1%
0.976
 
< 0.1%
0.966
 
< 0.1%
0.9510
 
< 0.1%
0.9416
 
< 0.1%
0.938
 
< 0.1%
0.9216
 
< 0.1%
0.918
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1426588517
Minimum0
Maximum1
Zeros255123
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:30.069337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3443796896
Coefficient of variation (CV)2.414008562
Kurtosis2.241135488
Mean0.1426588517
Median Absolute Deviation (MAD)0
Skewness2.050741506
Sum44371.04
Variance0.1185973706
MonotonicityNot monotonic
2021-12-24T21:29:30.200757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0255123
82.0%
139909
 
12.8%
0.033477
 
1.1%
0.042244
 
0.7%
0.011312
 
0.4%
0.021211
 
0.4%
0.05422
 
0.1%
0.99392
 
0.1%
0.95362
 
0.1%
0.07346
 
0.1%
Other values (91)6231
 
2.0%
ValueCountFrequency (%)
0255123
82.0%
0.011312
 
0.4%
0.021211
 
0.4%
0.033477
 
1.1%
0.042244
 
0.7%
0.05422
 
0.1%
0.06331
 
0.1%
0.07346
 
0.1%
0.0898
 
< 0.1%
0.0992
 
< 0.1%
ValueCountFrequency (%)
139909
12.8%
0.99392
 
0.1%
0.98199
 
0.1%
0.97123
 
< 0.1%
0.96299
 
0.1%
0.95362
 
0.1%
0.94160
 
0.1%
0.93238
 
0.1%
0.9260
 
< 0.1%
0.9191
 
< 0.1%

dst_host_srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1416933469
Minimum0
Maximum1
Zeros262774
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2021-12-24T21:29:30.346812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3465732718
Coefficient of variation (CV)2.445938919
Kurtosis2.255712699
Mean0.1416933469
Median Absolute Deviation (MAD)0
Skewness2.05870432
Sum44070.74
Variance0.1201130327
MonotonicityNot monotonic
2021-12-24T21:29:30.500086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0262774
84.5%
142913
 
13.8%
0.02847
 
0.3%
0.01790
 
0.3%
0.03378
 
0.1%
0.05360
 
0.1%
0.04341
 
0.1%
0.07246
 
0.1%
0.06238
 
0.1%
0.9891
 
< 0.1%
Other values (90)2051
 
0.7%
ValueCountFrequency (%)
0262774
84.5%
0.01790
 
0.3%
0.02847
 
0.3%
0.03378
 
0.1%
0.04341
 
0.1%
0.05360
 
0.1%
0.06238
 
0.1%
0.07246
 
0.1%
0.0865
 
< 0.1%
0.0942
 
< 0.1%
ValueCountFrequency (%)
142913
13.8%
0.9891
 
< 0.1%
0.9756
 
< 0.1%
0.9613
 
< 0.1%
0.9519
 
< 0.1%
0.9423
 
< 0.1%
0.9310
 
< 0.1%
0.929
 
< 0.1%
0.916
 
< 0.1%
0.912
 
< 0.1%

deu_ruim_ou_nao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.0 MiB
DoS
229853 
normal
60593 
R2L
 
13781
Probe
 
4166
U2R
 
2636

Length

Max length6
Median length3
Mean length3.611232393
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowR2L
5th rowR2L

Common Values

ValueCountFrequency (%)
DoS229853
73.9%
normal60593
 
19.5%
R2L13781
 
4.4%
Probe4166
 
1.3%
U2R2636
 
0.8%

Length

2021-12-24T21:29:30.617183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T21:29:30.686112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
dos229853
73.9%
normal60593
 
19.5%
r2l13781
 
4.4%
probe4166
 
1.3%
u2r2636
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-24T21:29:11.092112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:26:51.400365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:26:57.172111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:03.051389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2021-12-24T21:27:18.787419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:24.148617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:29.579809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:35.237175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:40.773138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:46.406620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:51.890840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:57.337930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:02.970100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:08.933782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:14.297741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:19.782727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:25.403817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:31.132912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:36.947405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:42.352951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:47.996299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:53.665624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:59.126430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:04.579948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:10.229887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:16.128282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:26:56.709908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:02.630719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:08.158252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:13.375998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:18.995903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:24.349728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:29.781049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:35.449676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:41.153420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:46.608310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:52.096401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:57.545789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:03.212118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:09.142604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:14.499477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:19.985188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:25.613808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:31.348821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:37.158597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:42.559622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:48.216084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:53.873039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:59.327883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:04.785620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:10.637051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:16.330917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:26:56.939048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:02.845850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:08.386205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:13.574214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:19.203845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:24.550114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:29.983375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:35.660524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:41.366627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:46.808515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:52.296780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:27:57.747420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:03.449158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:09.350893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:14.703384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:20.187241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:25.826738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:31.562134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:37.367173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:42.766430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:48.436598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:54.075710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:28:59.529495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:04.988925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2021-12-24T21:29:10.842108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2021-12-24T21:29:30.816125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-24T21:29:31.301611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-24T21:29:31.904656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-24T21:29:32.312809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-24T21:29:32.571039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-24T21:29:16.940385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-24T21:29:18.956306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratedeu_ruim_ou_nao
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20udpprivateSF1051460000000000000000110.00.00.00.01.00.00.02552541.000.010.000.000.00.00.00.0normal
30udpprivateSF1051460000000000000000220.00.00.00.01.00.00.02552541.000.010.000.000.00.00.00.0R2L
40udpprivateSF1051460000000000000000220.00.00.00.01.00.00.02552541.000.010.010.000.00.00.00.0R2L
50udpprivateSF1051460000000000000000220.00.00.00.01.00.00.02552551.000.000.010.000.00.00.00.0R2L
60udpdomain_uSF2900000000000000000210.00.00.00.00.51.00.01030.300.300.300.000.00.00.00.0normal
70udpprivateSF1051460000000000000000110.00.00.00.01.00.00.02552530.990.010.000.000.00.00.00.0normal
80udpprivateSF1051460000000000000000220.00.00.00.01.00.00.02552541.000.010.000.000.00.00.00.0R2L
90tcphttpSF2231850000010000000000440.00.00.00.01.00.00.0712551.000.000.010.010.00.00.00.0normal

Last rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratedeu_ruim_ou_nao
3110190udpprivateSF1051050000000000000000110.00.00.00.01.00.00.02552551.00.00.000.00.00.00.00.0R2L
3110200udpprivateSF1051050000000000000000330.00.00.00.01.00.00.02552551.00.00.000.00.00.00.00.0normal
3110210udpprivateSF1051050000000000000000110.00.00.00.01.00.00.02552551.00.00.000.00.00.00.00.0R2L
3110220udpprivateSF1051050000000000000000330.00.00.00.01.00.00.02552551.00.00.000.00.00.00.00.0normal
3110230udpprivateSF1051050000000000000000110.00.00.00.01.00.00.02552551.00.00.000.00.00.00.00.0R2L
3110240udpprivateSF1051470000000000000000220.00.00.00.01.00.00.02552551.00.00.010.00.00.00.00.0normal
3110250udpprivateSF1051470000000000000000440.00.00.00.01.00.00.02552551.00.00.010.00.00.00.00.0normal
3110260udpprivateSF1051470000000000000000220.00.00.00.01.00.00.02552551.00.00.010.00.00.00.00.0normal
3110270udpprivateSF1051470000000000000000440.00.00.00.01.00.00.02552551.00.00.010.00.00.00.00.0normal
3110280udpprivateSF1051470000000000000000220.00.00.00.01.00.00.02552551.00.00.010.00.00.00.00.0normal

Duplicate rows

Most frequently occurring

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratedeu_ruim_ou_nao# duplicates
3580icmpecr_iSF1032000000000000000005115110.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS56608
960icmpecr_iSF520000000000000000005115110.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS38572
3560icmpecr_iSF1032000000000000000005105100.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS13367
890icmpecr_iSF508000000000000000005115110.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS6114
950icmpecr_iSF520000000000000000005105100.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS5960
3520icmpecr_iSF1032000000000000000005095090.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS4619
940icmpecr_iSF520000000000000000005095090.00.00.00.01.00.00.02552551.000.001.00.00.00.00.00.0DoS2845
103810udpprivateSF10500000000000000000110.00.00.00.01.00.00.02552530.990.010.00.00.00.00.00.0normal2450
104680udpprivateSF1051460000000000000000110.00.00.00.01.00.00.02552541.000.010.00.00.00.00.00.0R2L1396
104900udpprivateSF1051460000000000000000220.00.00.00.01.00.00.02552541.000.010.00.00.00.00.00.0normal1354